The air quality monitoring network in Alaska is currently limited to ground-based observations in urban areas and national parks leaving a large proportion of the state unmonitored. The use of MODIS aerosol optical depth (AOD) to estimate ground-level particulate pollution concentrations has been successfully demonstrated around the world, and could potentially be used in Alaska. In this work, MODIS AOD measurements at 550 nm were validated against AOD derived from AERONET ground-based sunphotometers in Barrow and Bonanza Creek to determine if MODIS AOD from the Terra and Aqua satellites could be used to estimate ground-level particulate pollution concentrations. The MODIS AOD was obtained from MODIS collection 6 using the dark target Land and Ocean algorithms from 2000 to 2014. MODIS data could only be obtained between the months of April and October; therefore, it could only be validated for those months. Individual and combined Terra and Aqua MODIS data were considered. The results showed that MODIS collection 6 products at 10 km resolution for Terra and Aqua combined are not valid over land but are valid over the ocean. On the other hand, the individual Terra and Aqua MODIS collection 6 AOD products at 10 km resolution are valid over land individually but not when combined. Results also suggest the MODIS collection 6 AOD products at 3 km resolution are valid over land and ocean and perform better over land than the 10-km product. These findings indicate that MODIS collection 6 AOD products can be used quantitatively in air quality applications in Alaska during the summer months.

The climate change impacts on some regions of the planet faster and stronger. These areas are known as the hot spots for climate change and Cyprus (Nicosia) in the Mediterranean is one of these spots. This paper aims to analyze the significant changes of atmospheric aerosol characteristics in 2019 and during the extreme event of 25 April 2019. We study the aerosol optical thickness (AOT), Ångström exponent, single scattering albedo, refractive index (imaginary and real parts), size, and vertical distribution of aerosol particles during the event of a high atmospheric aerosol contamination over Nicosia in details. For this purpose, we used the ground-based lidar, observations of the sun-photometer AERONET Nicosia station, satellite products from the Moderate Resolution Imaging Spectroradiometer (MODIS), and back trajectories of air movements calculated using the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT). On 23–25 April, according to lidar and sun-photometer observations, strong aerosol pollution over Nicosia was detected. On April 25, 2019, the AOT value exceeds 1.0 at λ = 440 nm. Analysis of the optical and microphysical characteristics supported that the pollution consists of mainly Saharan dust and partly urban aerosols. This assumption was confirmed by HYSPLIT backward trajectories and MODIS images where air masses containing dust particles came from North Africa and from the Eastern part of Europe.

Aerosols play an important role in Earth’s climate system, thus long time ground based measurements of aerosol optical properties are useful in understanding this role. 10 years of quality-assured measurements between 2010 and 2020 are used to investigate the aerosol climatology in the Cluj-Napoca area, in North-Western Romania. In this study, we analyze the Aerosol Optical Depth (AOD), Single Scattering Albedo (SSA) and Ångström exponent obtained by CIMEL sun photometer, part of the Aerosol Robotic Network (AERONET) to extract the seasonality of aerosols in the region and investigate the aerosol climatology of the area. Higher aerosol loads are found during July and August. Ångström exponent has the lowest values in April and May and the highest in August. The classification of aerosols using AERONET data is performed to separate dust, biomass burning, polluted urban, marine and continental dominant aerosol mixtures. In addition, the study presents the validation efforts of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) dataset against AERONET AOD over 10 years period.

Extraordinary high aerosol contamination observed in the atmosphere over Kyiv city, Ukraine, during the March – April 2020 period. The source of contamination was the large grass and forest fires in the northern part of Ukraine and the Kyiv region. The level of PM2.5 load investigated using newly established AirVisual sensors mini-network in five areas of the city. The aerosol data from the Kyiv AERONET sun-photometer site analyzed for that period. Aerosol optical depth, Angstrom exponent, and the aerosol particles properties (particles size distribution, single-scattering albedo, and complex refractive index) were analyzed using AERONET sun-photometer observations. The smoke particles observed at Kyiv site during the fires in general correspond to aerosol with optical properties of biomass burning particles. The variability of the optical properties and chemical composition indicates that the aerosol particles in the smoke plumes over Kyiv were produced by different burning material and phases of vegetation fires at different time. The case of enormous PM2.5 aerosol contamination in Kyiv city reveals the need to accept strong measures for forest fire control and prevention in Kyiv region, especially in the north-west region where radioactive contamination from Chornobyl disaster is still significant.

In the presence of absorbing aerosol in the atmosphere a number of systematic errors of standard Ocean Color algorithms were noted, for example, negative values of remote sensing reflectance in the short-wavelength region at 412 nm and 443 nm. The main goal of this work is to develop an algorithm for additional correction of remote sensing reflectance level 2 satellite data, taking into account the presence of absorbing aerosol over the Black Sea, where a large number of dust transfers from the Sahara are observed annually. To implement the algorithm, an analytical and experimental evaluation of the interpolation function is carried out, taking into account the optical effects caused by the stratification of the absorbing aerosol. This algorithm is based on the constancy of the color index value, characteristic of the selected region. For the Black Sea the average value of CI(412/443) = 0.80±0.08, a small standard deviation indicates that the sample is slightly variable. Therefore, CI(412/443) = 0.80 will be further considered as the reference value of the color index for calculating new restored Rrs(λ).

Geo-information about the spectral variability of the water-leaving radiance is the key to the validation of satellite and in situ measurements and the development of regional algorithms. In this study, using cluster analysis, five trends were identified that are characteristic of various phenomena in the northwestern part of the Black Sea (summer and winter blooms, turbid waters, river runoff). Typical values of the remote sensing reflectance coefficient are calculated for each case. Additionally, the standard values of the color indices for each cluster are calculated. It is shown that the ratio of the color index CI(412/443 nm) is slightly variable and equals 0.8±0.07.This can be a reference point for recovering incorrect (negative) satellite Rrs(λ) values in the shortwave region. In a similar way, the color index was calculated according to the MODIS and VIRS data, it was shown that on days with a turbid atmosphere (high AOT values), the standard deviation of the color index is 30%. For days with a clear atmosphere, the color index is close to the in situ results.

The Advanced Meteorological Image (AMI) onboard GEOKOMPSAT 2A (GK-2A) enables the retrieval of dust aerosol optical depth (DAOD) from geostationary satellites using infrared (IR) channels. IR observations allow the retrieval of DAOD and the dust layer altitude (24 hour) over surface properties, particularly over deserts. In this study, dust events in northeast Asia from 2020 to 2021 were investigated using five GK-2A thermal IR bands (8.7, 10.5, 11.4, 12.3, and 13.3 μm). For the dust cloud, the observed brightness temperature differences (BTDs) of 10.5 and 12.3 μm were found to be always negative, while the BTD of 8.7 and 10.5 μm varies relying on the dust intensity. Making the best use of optical properties, a physical approach for DAOD lookup tables (LUTs) was developed using IR channels to retrieve the DAOD. This study simulated the characteristics of thermal radiation transfer using the forward model and dust aerosols can explain by BTD (10.5, 12.3 μm), which is an intrinsic characteristic of dust aerosol. The DAOD and property of dust can be gained from the brightness temperature (BT) of 10.5 μm and the BTD of 10.5, 12.3 μm. Additionally, the cumulative distribution function (CDF) was employed to strengthen the continuity of 24-hour DAOD, which was applied to the algorithm by calculating the conversion value coefficient for the DAOD error correction of the IR with daytime visible aerosol optical depth (VAOD) as the true value. The results show that DAOD product can be successfully used during both daytime and nighttime to continuously monitor the flow of yellow dust from the GK-2A satellite in northeast Asia. In particular, the validation result of IR DAOD was similar to that of the active satellite product (CALIPSO/CALIOP), which showed a tendency similar to that for IR DAOD at night.